Overview

Dataset statistics

Number of variables40
Number of observations2939478
Missing cells58016800
Missing cells (%)49.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory897.1 MiB
Average record size in memory320.0 B

Variable types

Categorical16
Numeric16
Unsupported8

Alerts

id_mutation has a high cardinality: 1274243 distinct values High cardinality
date_mutation has a high cardinality: 366 distinct values High cardinality
adresse_nom_voie has a high cardinality: 445200 distinct values High cardinality
adresse_code_voie has a high cardinality: 14726 distinct values High cardinality
nom_commune has a high cardinality: 30417 distinct values High cardinality
ancien_nom_commune has a high cardinality: 1772 distinct values High cardinality
id_parcelle has a high cardinality: 1820762 distinct values High cardinality
ancien_id_parcelle has a high cardinality: 25690 distinct values High cardinality
code_nature_culture_speciale has a high cardinality: 124 distinct values High cardinality
nature_culture_speciale has a high cardinality: 124 distinct values High cardinality
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot4_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 4 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot5_surface_carrezHigh correlation
lot2_surface_carrez is highly correlated with lot4_surface_carrezHigh correlation
lot3_surface_carrez is highly correlated with surface_reelle_batiHigh correlation
lot4_surface_carrez is highly correlated with lot2_surface_carrezHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrezHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot3_surface_carrezHigh correlation
nombre_pieces_principales is highly correlated with code_type_localHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot4_surface_carrezHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
numero_disposition is highly correlated with nature_mutationHigh correlation
nature_mutation is highly correlated with numero_dispositionHigh correlation
valeur_fonciere is highly correlated with lot3_surface_carrezHigh correlation
adresse_numero is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
adresse_suffixe is highly correlated with adresse_numeroHigh correlation
code_postal is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot3_surface_carrez and 2 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with valeur_fonciere and 4 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with adresse_numero and 4 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 1 other fieldsHigh correlation
nombre_lots is highly correlated with lot3_surface_carrezHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
longitude is highly correlated with code_postal and 1 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
valeur_fonciere has 36638 (1.2%) missing values Missing
adresse_numero has 1257084 (42.8%) missing values Missing
adresse_suffixe has 2816323 (95.8%) missing values Missing
ancien_code_commune has 2847076 (96.9%) missing values Missing
ancien_nom_commune has 2847076 (96.9%) missing values Missing
ancien_id_parcelle has 2907846 (98.9%) missing values Missing
numero_volume has 2927451 (99.6%) missing values Missing
lot1_numero has 2050131 (69.7%) missing values Missing
lot1_surface_carrez has 2700907 (91.9%) missing values Missing
lot2_numero has 2747922 (93.5%) missing values Missing
lot2_surface_carrez has 2879425 (98.0%) missing values Missing
lot3_numero has 2907565 (98.9%) missing values Missing
lot3_surface_carrez has 2933591 (99.8%) missing values Missing
lot4_numero has 2928634 (99.6%) missing values Missing
lot4_surface_carrez has 2938019 (> 99.9%) missing values Missing
lot5_numero has 2934306 (99.8%) missing values Missing
lot5_surface_carrez has 2938834 (> 99.9%) missing values Missing
code_type_local has 1350027 (45.9%) missing values Missing
type_local has 1350027 (45.9%) missing values Missing
surface_reelle_bati has 1763264 (60.0%) missing values Missing
nombre_pieces_principales has 1352568 (46.0%) missing values Missing
code_nature_culture has 901816 (30.7%) missing values Missing
nature_culture has 901816 (30.7%) missing values Missing
code_nature_culture_speciale has 2800349 (95.3%) missing values Missing
nature_culture_speciale has 2800349 (95.3%) missing values Missing
surface_terrain has 901874 (30.7%) missing values Missing
longitude has 112870 (3.8%) missing values Missing
latitude has 112870 (3.8%) missing values Missing
numero_disposition is highly skewed (γ1 = 35.02589871) Skewed
lot1_surface_carrez is highly skewed (γ1 = 27.63008812) Skewed
lot2_surface_carrez is highly skewed (γ1 = 33.39231458) Skewed
lot3_surface_carrez is highly skewed (γ1 = 38.11228458) Skewed
nombre_lots is highly skewed (γ1 = 82.80203865) Skewed
surface_reelle_bati is highly skewed (γ1 = 101.1116364) Skewed
surface_terrain is highly skewed (γ1 = 121.0253572) Skewed
code_commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
numero_volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot1_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot2_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot3_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot4_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot5_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
nombre_lots has 2050131 (69.7%) zeros Zeros
nombre_pieces_principales has 504465 (17.2%) zeros Zeros

Reproduction

Analysis started2021-10-05 22:34:38.415698
Analysis finished2021-10-05 22:48:21.636508
Duration13 minutes and 43.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id_mutation
Categorical

HIGH CARDINALITY

Distinct1274243
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2016-193380
 
5545
2016-1154770
 
3756
2016-1210409
 
2608
2016-891506
 
2206
2016-891518
 
1698
Other values (1274238)
2923665 

Length

Max length12
Median length11
Mean length11.10557147
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique622975 ?
Unique (%)21.2%

Sample

1st row2016-1
2nd row2016-2
3rd row2016-2
4th row2016-2
5th row2016-2

Common Values

ValueCountFrequency (%)
2016-1933805545
 
0.2%
2016-11547703756
 
0.1%
2016-12104092608
 
0.1%
2016-8915062206
 
0.1%
2016-8915181698
 
0.1%
2016-11728571570
 
0.1%
2016-9624491397
 
< 0.1%
2016-12157411354
 
< 0.1%
2016-7600571253
 
< 0.1%
2016-11760571237
 
< 0.1%
Other values (1274233)2916854
99.2%

Length

2021-10-06T00:48:21.861558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-1933805545
 
0.2%
2016-11547703756
 
0.1%
2016-12104092608
 
0.1%
2016-8915062206
 
0.1%
2016-8915181698
 
0.1%
2016-11728571570
 
0.1%
2016-9624491397
 
< 0.1%
2016-12157411354
 
< 0.1%
2016-7600571253
 
< 0.1%
2016-11760571237
 
< 0.1%
Other values (1274233)2916854
99.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_mutation
Categorical

HIGH CARDINALITY

Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2016-12-30
 
31580
2016-12-29
 
30141
2016-06-30
 
27563
2016-12-22
 
25443
2016-12-16
 
23455
Other values (361)
2801296 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016-01-08
2nd row2016-01-11
3rd row2016-01-11
4th row2016-01-11
5th row2016-01-11

Common Values

ValueCountFrequency (%)
2016-12-3031580
 
1.1%
2016-12-2930141
 
1.0%
2016-06-3027563
 
0.9%
2016-12-2225443
 
0.9%
2016-12-1623455
 
0.8%
2016-09-3023203
 
0.8%
2016-12-2122608
 
0.8%
2016-07-2922504
 
0.8%
2016-12-1520443
 
0.7%
2016-12-2020252
 
0.7%
Other values (356)2692286
91.6%

Length

2021-10-06T00:48:22.133635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-12-3031580
 
1.1%
2016-12-2930141
 
1.0%
2016-06-3027563
 
0.9%
2016-12-2225443
 
0.9%
2016-12-1623455
 
0.8%
2016-09-3023203
 
0.8%
2016-12-2122608
 
0.8%
2016-07-2922504
 
0.8%
2016-12-1520443
 
0.7%
2016-12-2020252
 
0.7%
Other values (356)2692286
91.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_disposition
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct964
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.111543274
Minimum1
Maximum1271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:22.421701image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum1271
Range1270
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.04074981
Coefficient of variation (CV)12.33256743
Kurtosis1375.699129
Mean2.111543274
Median Absolute Deviation (MAD)0
Skewness35.02589871
Sum6206835
Variance678.1206508
MonotonicityNot monotonic
2021-10-06T00:48:22.771839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12687305
91.4%
2189624
 
6.5%
335645
 
1.2%
46592
 
0.2%
184305
 
0.1%
52082
 
0.1%
61028
 
< 0.1%
45557
 
< 0.1%
7514
 
< 0.1%
8444
 
< 0.1%
Other values (954)11382
 
0.4%
ValueCountFrequency (%)
12687305
91.4%
2189624
 
6.5%
335645
 
1.2%
46592
 
0.2%
52082
 
0.1%
61028
 
< 0.1%
7514
 
< 0.1%
8444
 
< 0.1%
9444
 
< 0.1%
10358
 
< 0.1%
ValueCountFrequency (%)
127131
< 0.1%
127031
< 0.1%
126425
< 0.1%
126225
< 0.1%
125922
< 0.1%
125622
< 0.1%
124819
< 0.1%
124419
< 0.1%
124117
< 0.1%
123716
< 0.1%

nature_mutation
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
Vente
2644109 
Vente en l'état futur d'achèvement
 
216603
Echange
 
47199
Adjudication
 
14722
Expropriation
 
8612

Length

Max length34
Median length5
Mean length7.272363665
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVente
2nd rowVente
3rd rowVente
4th rowVente
5th rowVente

Common Values

ValueCountFrequency (%)
Vente2644109
90.0%
Vente en l'état futur d'achèvement216603
 
7.4%
Echange47199
 
1.6%
Adjudication14722
 
0.5%
Expropriation8612
 
0.3%
Vente terrain à bâtir8233
 
0.3%

Length

2021-10-06T00:48:23.077909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:48:23.256951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
vente2868945
74.9%
d'achèvement216603
 
5.7%
futur216603
 
5.7%
l'état216603
 
5.7%
en216603
 
5.7%
echange47199
 
1.2%
adjudication14722
 
0.4%
expropriation8612
 
0.2%
bâtir8233
 
0.2%
à8233
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valeur_fonciere
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct119790
Distinct (%)4.1%
Missing36638
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1023719.823
Minimum0.06
Maximum396000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:23.578321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile2000
Q150500
median135000
Q3242210.9
95-th percentile1200000
Maximum396000000
Range395999999.9
Interquartile range (IQR)191710.9

Descriptive statistics

Standard deviation8817792.946
Coefficient of variation (CV)8.613482664
Kurtosis360.6564053
Mean1023719.823
Median Absolute Deviation (MAD)92000
Skewness17.37404568
Sum2.97169485 × 1012
Variance7.775347243 × 1013
MonotonicityNot monotonic
2021-10-06T00:48:23.908395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000027965
 
1.0%
15000026475
 
0.9%
125541
 
0.9%
12000024934
 
0.8%
8000023571
 
0.8%
5000022756
 
0.8%
11000021872
 
0.7%
6000021730
 
0.7%
13000021655
 
0.7%
9000021210
 
0.7%
Other values (119780)2665131
90.7%
(Missing)36638
 
1.2%
ValueCountFrequency (%)
0.065
 
< 0.1%
0.091
 
< 0.1%
0.14
 
< 0.1%
0.121
 
< 0.1%
0.15897
< 0.1%
0.1611
 
< 0.1%
0.18294
 
< 0.1%
0.198
 
< 0.1%
0.218
 
< 0.1%
0.231
 
< 0.1%
ValueCountFrequency (%)
3960000008
 
< 0.1%
39437715219
< 0.1%
3780000001
 
< 0.1%
3721004163
 
< 0.1%
33376000034
< 0.1%
3300000003
 
< 0.1%
2856000001
 
< 0.1%
26761100024
< 0.1%
2648310081
 
< 0.1%
25148076817
< 0.1%

adresse_numero
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6938
Distinct (%)0.4%
Missing1257084
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean813.2028389
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:24.298028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median25
Q399
95-th percentile5800
Maximum9999
Range9998
Interquartile range (IQR)91

Descriptive statistics

Standard deviation2166.810145
Coefficient of variation (CV)2.664538343
Kurtosis6.704854514
Mean813.2028389
Median Absolute Deviation (MAD)21
Skewness2.808791473
Sum1368127577
Variance4695066.204
MonotonicityNot monotonic
2021-10-06T00:48:24.620853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174899
 
2.5%
266997
 
2.3%
353312
 
1.8%
451751
 
1.8%
548119
 
1.6%
647861
 
1.6%
742437
 
1.4%
840663
 
1.4%
1037255
 
1.3%
936216
 
1.2%
Other values (6928)1182884
40.2%
(Missing)1257084
42.8%
ValueCountFrequency (%)
174899
2.5%
266997
2.3%
353312
1.8%
451751
1.8%
548119
1.6%
647861
1.6%
742437
1.4%
840663
1.4%
936216
1.2%
1037255
1.3%
ValueCountFrequency (%)
9999317
< 0.1%
999841
 
< 0.1%
99976
 
< 0.1%
99969
 
< 0.1%
999510
 
< 0.1%
999412
 
< 0.1%
99935
 
< 0.1%
99923
 
< 0.1%
999131
 
< 0.1%
99906
 
< 0.1%

adresse_suffixe
Categorical

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)< 0.1%
Missing2816323
Missing (%)95.8%
Memory size22.4 MiB
B
69866 
A
20277 
T
10634 
F
10584 
C
 
4046
Other values (35)
7748 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowD
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
B69866
 
2.4%
A20277
 
0.7%
T10634
 
0.4%
F10584
 
0.4%
C4046
 
0.1%
D1962
 
0.1%
E1072
 
< 0.1%
Q839
 
< 0.1%
P712
 
< 0.1%
G500
 
< 0.1%
Other values (30)2663
 
0.1%
(Missing)2816323
95.8%

Length

2021-10-06T00:48:25.163484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b69866
56.7%
a20277
 
16.5%
t10634
 
8.6%
f10584
 
8.6%
c4046
 
3.3%
d1962
 
1.6%
e1072
 
0.9%
q839
 
0.7%
p712
 
0.6%
g500
 
0.4%
Other values (27)2663
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_nom_voie
Categorical

HIGH CARDINALITY

Distinct445200
Distinct (%)15.3%
Missing23338
Missing (%)0.8%
Memory size22.4 MiB
LE VILLAGE
 
28548
LE BOURG
 
24988
GR GRANDE RUE
 
5370
RUE DE LA REPUBLIQUE
 
5104
RUE JEAN JAURES
 
5043
Other values (445195)
2847087 

Length

Max length31
Median length14
Mean length14.61219969
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154082 ?
Unique (%)5.3%

Sample

1st rowRUE TONY REVILLON
2nd rowLES BROTTEAUX
3rd rowLES BROTTEAUX
4th rowLES BROTTEAUX
5th rowLES BROTTEAUX

Common Values

ValueCountFrequency (%)
LE VILLAGE28548
 
1.0%
LE BOURG24988
 
0.9%
GR GRANDE RUE5370
 
0.2%
RUE DE LA REPUBLIQUE5104
 
0.2%
RUE JEAN JAURES5043
 
0.2%
RUE PASTEUR4931
 
0.2%
AV JEAN JAURES4435
 
0.2%
AV DE LA REPUBLIQUE4152
 
0.1%
RUE VICTOR HUGO4147
 
0.1%
RUE DE PARIS3994
 
0.1%
Other values (445190)2825428
96.1%
(Missing)23338
 
0.8%

Length

2021-10-06T00:48:25.502663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue946585
 
11.4%
de626364
 
7.5%
la433378
 
5.2%
du289150
 
3.5%
le260414
 
3.1%
des241579
 
2.9%
av220383
 
2.6%
les208878
 
2.5%
che90125
 
1.1%
rte83050
 
1.0%
Other values (189414)4924560
59.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_code_voie
Categorical

HIGH CARDINALITY

Distinct14726
Distinct (%)0.5%
Missing23310
Missing (%)0.8%
Memory size22.4 MiB
B003
 
14637
B006
 
14144
B005
 
14034
B002
 
13888
B012
 
13788
Other values (14721)
2845677 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1170 ?
Unique (%)< 0.1%

Sample

1st row0560
2nd rowB011
3rd rowB011
4th rowB011
5th rowB011

Common Values

ValueCountFrequency (%)
B00314637
 
0.5%
B00614144
 
0.5%
B00514034
 
0.5%
B00213888
 
0.5%
B01213788
 
0.5%
B01113615
 
0.5%
B00113527
 
0.5%
B00913416
 
0.5%
B00813406
 
0.5%
B01513288
 
0.5%
Other values (14716)2778425
94.5%
(Missing)23310
 
0.8%

Length

2021-10-06T00:48:25.776246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b00314637
 
0.5%
b00614144
 
0.5%
b00514034
 
0.5%
b00213888
 
0.5%
b01213788
 
0.5%
b01113615
 
0.5%
b00113527
 
0.5%
b00913416
 
0.5%
b00813406
 
0.5%
b01513288
 
0.5%
Other values (14716)2778425
95.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_postal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5863
Distinct (%)0.2%
Missing23494
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean50558.51963
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:26.082813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6670
Q129250
median49370
Q375014
95-th percentile93160
Maximum97490
Range96490
Interquartile range (IQR)45764

Descriptive statistics

Standard deviation27494.99945
Coefficient of variation (CV)0.5438252476
Kurtosis-1.206952752
Mean50558.51963
Median Absolute Deviation (MAD)24040
Skewness-0.009250483748
Sum1.474278343 × 1011
Variance755974994.9
MonotonicityNot monotonic
2021-10-06T00:48:26.407397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350006801
 
0.2%
180006492
 
0.2%
210006229
 
0.2%
691006138
 
0.2%
921106041
 
0.2%
312005894
 
0.2%
921505793
 
0.2%
750165789
 
0.2%
750155757
 
0.2%
540005595
 
0.2%
Other values (5853)2855455
97.1%
(Missing)23494
 
0.8%
ValueCountFrequency (%)
10001625
0.1%
1090346
 
< 0.1%
1100883
< 0.1%
1110375
 
< 0.1%
1120709
< 0.1%
1130306
 
< 0.1%
1140439
 
< 0.1%
1150907
< 0.1%
1160696
< 0.1%
11701236
< 0.1%
ValueCountFrequency (%)
974901543
0.1%
97480648
< 0.1%
97470328
 
< 0.1%
97460462
 
< 0.1%
97450217
 
< 0.1%
9744255
 
< 0.1%
97441220
 
< 0.1%
97440354
 
< 0.1%
9743953
 
< 0.1%
97438426
 
< 0.1%

code_commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size22.4 MiB

nom_commune
Categorical

HIGH CARDINALITY

Distinct30417
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
Toulouse
 
23731
Nantes
 
15689
Bordeaux
 
14566
Nice
 
14181
Montpellier
 
13813
Other values (30412)
2857498 

Length

Max length45
Median length10
Mean length11.87023887
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique378 ?
Unique (%)< 0.1%

Sample

1st rowSaint-Laurent-sur-Saône
2nd rowVarambon
3rd rowVarambon
4th rowVarambon
5th rowVarambon

Common Values

ValueCountFrequency (%)
Toulouse23731
 
0.8%
Nantes15689
 
0.5%
Bordeaux14566
 
0.5%
Nice14181
 
0.5%
Montpellier13813
 
0.5%
Rennes10382
 
0.4%
Lille10349
 
0.4%
Nîmes6844
 
0.2%
Angers6771
 
0.2%
Bourges6492
 
0.2%
Other values (30407)2816660
95.8%

Length

2021-10-06T00:48:26.737473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement106553
 
3.1%
la90796
 
2.7%
le78397
 
2.3%
paris58411
 
1.7%
les30835
 
0.9%
marseille28187
 
0.8%
toulouse23731
 
0.7%
lyon19955
 
0.6%
nantes15689
 
0.5%
bordeaux14566
 
0.4%
Other values (30324)2940145
86.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size22.4 MiB

ancien_code_commune
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1783
Distinct (%)1.9%
Missing2847076
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean52147.84335
Minimum1025
Maximum95308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:27.073547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1025
5-th percentile14126
Q128103
median50097
Q374010
95-th percentile90068
Maximum95308
Range94283
Interquartile range (IQR)45907

Descriptive statistics

Standard deviation25880.59711
Coefficient of variation (CV)0.4962927601
Kurtosis-1.169859044
Mean52147.84335
Median Absolute Deviation (MAD)23913
Skewness-0.1370263323
Sum4818565021
Variance669805306.7
MonotonicityNot monotonic
2021-10-06T00:48:27.422625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
740106204
 
0.2%
912281554
 
0.1%
851941483
 
0.1%
930701362
 
< 0.1%
785511222
 
< 0.1%
953061023
 
< 0.1%
73257984
 
< 0.1%
85166934
 
< 0.1%
85060795
 
< 0.1%
74282668
 
< 0.1%
Other values (1773)76173
 
2.6%
(Missing)2847076
96.9%
ValueCountFrequency (%)
1025229
< 0.1%
1033518
< 0.1%
1036158
 
< 0.1%
10594
 
< 0.1%
1091215
< 0.1%
109577
 
< 0.1%
109722
 
< 0.1%
109832
 
< 0.1%
111912
 
< 0.1%
112223
 
< 0.1%
ValueCountFrequency (%)
9530812
 
< 0.1%
953061023
< 0.1%
952593
 
< 0.1%
950408
 
< 0.1%
930701362
< 0.1%
91390131
 
< 0.1%
912281554
0.1%
9122211
 
< 0.1%
91182453
 
< 0.1%
9007322
 
< 0.1%

ancien_nom_commune
Categorical

HIGH CARDINALITY
MISSING

Distinct1772
Distinct (%)1.9%
Missing2847076
Missing (%)96.9%
Memory size22.4 MiB
Annecy
 
6204
Évry
 
1554
Les Sables-d'Olonne
 
1483
Saint-Ouen
 
1362
Saint-Germain-en-Laye
 
1222
Other values (1767)
80577 

Length

Max length44
Median length11
Mean length12.41290232
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)0.1%

Sample

1st rowCras-sur-Reyssouze
2nd rowCras-sur-Reyssouze
3rd rowCras-sur-Reyssouze
4th rowÉtrez
5th rowBâgé-la-Ville

Common Values

ValueCountFrequency (%)
Annecy6204
 
0.2%
Évry1554
 
0.1%
Les Sables-d'Olonne1483
 
0.1%
Saint-Ouen1362
 
< 0.1%
Saint-Germain-en-Laye1222
 
< 0.1%
Herblay1023
 
< 0.1%
Les Belleville984
 
< 0.1%
Olonne-sur-Mer934
 
< 0.1%
Château-d'Olonne795
 
< 0.1%
Thorens-Glières668
 
< 0.1%
Other values (1762)76173
 
2.6%
(Missing)2847076
96.9%

Length

2021-10-06T00:48:27.796061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
annecy6204
 
5.9%
les3989
 
3.8%
la3057
 
2.9%
le2864
 
2.7%
évry1554
 
1.5%
sables-d'olonne1483
 
1.4%
saint-ouen1362
 
1.3%
belleville1261
 
1.2%
saint-germain-en-laye1222
 
1.2%
herblay1023
 
1.0%
Other values (1776)81937
77.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id_parcelle
Categorical

HIGH CARDINALITY

Distinct1820762
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
92073000AH0230
 
972
13106000BC0020
 
930
93010000AM0299
 
757
95277000ZS1580
 
728
92073000AR0385
 
684
Other values (1820757)
2935407 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1456963 ?
Unique (%)49.6%

Sample

1st row013700000A0253
2nd row014300000C1043
3rd row014300000C1157
4th row014300000C1159
5th row014300000C1160

Common Values

ValueCountFrequency (%)
92073000AH0230972
 
< 0.1%
13106000BC0020930
 
< 0.1%
93010000AM0299757
 
< 0.1%
95277000ZS1580728
 
< 0.1%
92073000AR0385684
 
< 0.1%
94041000AM0078656
 
< 0.1%
940160000N0096650
 
< 0.1%
77243000AS0132646
 
< 0.1%
30189000EM0022637
 
< 0.1%
84007000DP0120608
 
< 0.1%
Other values (1820752)2932210
99.8%

Length

2021-10-06T00:48:28.292682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
92073000ah0230972
 
< 0.1%
13106000bc0020930
 
< 0.1%
93010000am0299757
 
< 0.1%
95277000zs1580728
 
< 0.1%
92073000ar0385684
 
< 0.1%
94041000am0078656
 
< 0.1%
940160000n0096650
 
< 0.1%
77243000as0132646
 
< 0.1%
30189000em0022637
 
< 0.1%
84007000dp0120608
 
< 0.1%
Other values (1820752)2932210
99.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_id_parcelle
Categorical

HIGH CARDINALITY
MISSING

Distinct25690
Distinct (%)81.2%
Missing2907846
Missing (%)98.9%
Memory size22.4 MiB
85166000AC1258
 
82
91182000AB0141
 
61
78524000AB0102
 
54
85166000AW0368
 
43
85060000AI0349
 
40
Other values (25685)
31352 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22020 ?
Unique (%)69.6%

Sample

1st row01154000ZA0002
2nd row011440000B0330
3rd row011440000B0332
4th row011440000B0625
5th row011440000B0635

Common Values

ValueCountFrequency (%)
85166000AC125882
 
< 0.1%
91182000AB014161
 
< 0.1%
78524000AB010254
 
< 0.1%
85166000AW036843
 
< 0.1%
85060000AI034940
 
< 0.1%
91182000AN052834
 
< 0.1%
78524000AB000734
 
< 0.1%
782510000B025628
 
< 0.1%
78524000AB015227
 
< 0.1%
01091458ZB060026
 
< 0.1%
Other values (25680)31203
 
1.1%
(Missing)2907846
98.9%

Length

2021-10-06T00:48:28.541739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
85166000ac125882
 
0.3%
91182000ab014161
 
0.2%
78524000ab010254
 
0.2%
85166000aw036843
 
0.1%
85060000ai034940
 
0.1%
91182000an052834
 
0.1%
78524000ab000734
 
0.1%
782510000b025628
 
0.1%
78524000ab015227
 
0.1%
01091458zb060026
 
0.1%
Other values (25680)31203
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2927451
Missing (%)99.6%
Memory size22.4 MiB

lot1_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2050131
Missing (%)69.7%
Memory size22.4 MiB

lot1_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct17326
Distinct (%)7.3%
Missing2700907
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean65.50236923
Minimum0.01
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:28.794812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile16.9
Q133.85
median53.5
Q373.67
95-th percentile117.44
Maximum9999
Range9998.99
Interquartile range (IQR)39.82

Descriptive statistics

Standard deviation160.7394337
Coefficient of variation (CV)2.453948393
Kurtosis946.3559606
Mean65.50236923
Median Absolute Deviation (MAD)19.86
Skewness27.63008812
Sum15626965.73
Variance25837.16555
MonotonicityNot monotonic
2021-10-06T00:48:29.079384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12328
 
< 0.1%
12.5317
 
< 0.1%
15300
 
< 0.1%
67289
 
< 0.1%
60282
 
< 0.1%
30279
 
< 0.1%
65275
 
< 0.1%
45270
 
< 0.1%
40267
 
< 0.1%
70257
 
< 0.1%
Other values (17316)235707
 
8.0%
(Missing)2700907
91.9%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.33
 
< 0.1%
0.367
 
< 0.1%
0.72
 
< 0.1%
0.732
 
< 0.1%
0.771
 
< 0.1%
0.83
 
< 0.1%
0.91
 
< 0.1%
0.991
 
< 0.1%
127
< 0.1%
ValueCountFrequency (%)
99992
< 0.1%
91641
< 0.1%
84321
< 0.1%
81541
< 0.1%
79331
< 0.1%
75871
< 0.1%
75491
< 0.1%
75001
< 0.1%
72561
< 0.1%
69691
< 0.1%

lot2_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2747922
Missing (%)93.5%
Memory size22.4 MiB

lot2_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct11781
Distinct (%)19.6%
Missing2879425
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean65.99463807
Minimum0.01
Maximum6792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:29.362955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile22.85
Q143.03
median61.07
Q376.31
95-th percentile110.63
Maximum6792
Range6791.99
Interquartile range (IQR)33.28

Descriptive statistics

Standard deviation107.9081452
Coefficient of variation (CV)1.635104736
Kurtosis1434.387619
Mean65.99463807
Median Absolute Deviation (MAD)16.73
Skewness33.39231458
Sum3963176
Variance11644.16781
MonotonicityNot monotonic
2021-10-06T00:48:29.647534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6580
 
< 0.1%
4071
 
< 0.1%
6270
 
< 0.1%
6767
 
< 0.1%
6366
 
< 0.1%
7065
 
< 0.1%
6464
 
< 0.1%
7259
 
< 0.1%
5556
 
< 0.1%
5755
 
< 0.1%
Other values (11771)59400
 
2.0%
(Missing)2879425
98.0%
ValueCountFrequency (%)
0.011
< 0.1%
0.361
< 0.1%
0.511
< 0.1%
0.571
< 0.1%
0.72
< 0.1%
0.732
< 0.1%
0.752
< 0.1%
0.831
< 0.1%
0.91
< 0.1%
0.931
< 0.1%
ValueCountFrequency (%)
67921
< 0.1%
65711
< 0.1%
65541
< 0.1%
57381
< 0.1%
55321
< 0.1%
49801
< 0.1%
44911
< 0.1%
38881
< 0.1%
38031
< 0.1%
36061
< 0.1%

lot3_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2907565
Missing (%)98.9%
Memory size22.4 MiB

lot3_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4365
Distinct (%)74.1%
Missing2933591
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean74.47500425
Minimum0.27
Maximum7783.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:30.009174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile12.5
Q138.2
median61.56
Q386.29
95-th percentile157.684
Maximum7783.9
Range7783.63
Interquartile range (IQR)48.09

Descriptive statistics

Standard deviation133.2677603
Coefficient of variation (CV)1.789429375
Kurtosis2026.815321
Mean74.47500425
Median Absolute Deviation (MAD)23.93
Skewness38.11228458
Sum438434.35
Variance17760.29593
MonotonicityNot monotonic
2021-10-06T00:48:30.478307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9821
 
< 0.1%
12.520
 
< 0.1%
56.218
 
< 0.1%
8013
 
< 0.1%
1211
 
< 0.1%
3510
 
< 0.1%
3010
 
< 0.1%
409
 
< 0.1%
208
 
< 0.1%
22.928
 
< 0.1%
Other values (4355)5759
 
0.2%
(Missing)2933591
99.8%
ValueCountFrequency (%)
0.271
 
< 0.1%
0.281
 
< 0.1%
0.362
< 0.1%
0.741
 
< 0.1%
0.751
 
< 0.1%
0.831
 
< 0.1%
0.853
< 0.1%
12
< 0.1%
1.11
 
< 0.1%
1.351
 
< 0.1%
ValueCountFrequency (%)
7783.91
< 0.1%
3916.31
< 0.1%
12851
< 0.1%
1239.31
< 0.1%
1217.71
< 0.1%
1206.21
< 0.1%
1102.161
< 0.1%
10851
< 0.1%
10281
< 0.1%
884.981
< 0.1%

lot4_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2928634
Missing (%)99.6%
Memory size22.4 MiB

lot4_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1258
Distinct (%)86.2%
Missing2938019
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean82.64703221
Minimum0.36
Maximum1612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:30.793377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile8.98
Q130.11
median60.85
Q395.005
95-th percentile213.747
Maximum1612
Range1611.64
Interquartile range (IQR)64.895

Descriptive statistics

Standard deviation111.5239584
Coefficient of variation (CV)1.349400643
Kurtosis55.02835672
Mean82.64703221
Median Absolute Deviation (MAD)31.85
Skewness6.170102522
Sum120582.02
Variance12437.59329
MonotonicityNot monotonic
2021-10-06T00:48:31.134454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.821
 
< 0.1%
56.8210
 
< 0.1%
18.167
 
< 0.1%
12.56
 
< 0.1%
216
 
< 0.1%
14.845
 
< 0.1%
22.75
 
< 0.1%
658.075
 
< 0.1%
154
 
< 0.1%
59.994
 
< 0.1%
Other values (1248)1386
 
< 0.1%
(Missing)2938019
> 99.9%
ValueCountFrequency (%)
0.361
< 0.1%
0.71
< 0.1%
0.831
< 0.1%
11
< 0.1%
1.321
< 0.1%
1.361
< 0.1%
22
< 0.1%
2.171
< 0.1%
2.71
< 0.1%
31
< 0.1%
ValueCountFrequency (%)
16121
 
< 0.1%
1295.51
 
< 0.1%
1217.11
 
< 0.1%
884.982
 
< 0.1%
883.51
 
< 0.1%
881.282
 
< 0.1%
8601
 
< 0.1%
727.871
 
< 0.1%
661.911
 
< 0.1%
658.075
< 0.1%

lot5_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2934306
Missing (%)99.8%
Memory size22.4 MiB

lot5_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct547
Distinct (%)84.9%
Missing2938834
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean84.06465839
Minimum1.35
Maximum1328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:31.500061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.35
5-th percentile6.66
Q121.8
median56.53
Q399.4225
95-th percentile265.303
Maximum1328
Range1326.65
Interquartile range (IQR)77.6225

Descriptive statistics

Standard deviation107.8456248
Coefficient of variation (CV)1.282888992
Kurtosis36.94631522
Mean84.06465839
Median Absolute Deviation (MAD)35.845
Skewness4.736788302
Sum54137.64
Variance11630.67879
MonotonicityNot monotonic
2021-10-06T00:48:31.807641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6621
 
< 0.1%
34.477
 
< 0.1%
21.446
 
< 0.1%
22.75
 
< 0.1%
59.924
 
< 0.1%
84
 
< 0.1%
23
 
< 0.1%
603
 
< 0.1%
703
 
< 0.1%
123
 
< 0.1%
Other values (537)585
 
< 0.1%
(Missing)2938834
> 99.9%
ValueCountFrequency (%)
1.351
 
< 0.1%
1.631
 
< 0.1%
23
< 0.1%
2.881
 
< 0.1%
31
 
< 0.1%
3.71
 
< 0.1%
3.81
 
< 0.1%
3.851
 
< 0.1%
3.921
 
< 0.1%
4.041
 
< 0.1%
ValueCountFrequency (%)
13281
< 0.1%
8601
< 0.1%
6991
< 0.1%
658.072
< 0.1%
650.021
< 0.1%
548.671
< 0.1%
543.721
< 0.1%
475.61
< 0.1%
418.571
< 0.1%
401.741
< 0.1%

nombre_lots
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3904628645
Minimum0
Maximum468
Zeros2050131
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:32.114718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum468
Range468
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8944235613
Coefficient of variation (CV)2.2906751
Kurtosis30236.37693
Mean0.3904628645
Median Absolute Deviation (MAD)0
Skewness82.80203865
Sum1147757
Variance0.799993507
MonotonicityNot monotonic
2021-10-06T00:48:32.474800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02050131
69.7%
1697791
 
23.7%
2159643
 
5.4%
321069
 
0.7%
45672
 
0.2%
52122
 
0.1%
61068
 
< 0.1%
7515
 
< 0.1%
8365
 
< 0.1%
9205
 
< 0.1%
Other values (80)897
 
< 0.1%
ValueCountFrequency (%)
02050131
69.7%
1697791
 
23.7%
2159643
 
5.4%
321069
 
0.7%
45672
 
0.2%
52122
 
0.1%
61068
 
< 0.1%
7515
 
< 0.1%
8365
 
< 0.1%
9205
 
< 0.1%
ValueCountFrequency (%)
4681
< 0.1%
2231
< 0.1%
1841
< 0.1%
1701
< 0.1%
1501
< 0.1%
1491
< 0.1%
1471
< 0.1%
1211
< 0.1%
1202
< 0.1%
1181
< 0.1%

code_type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1350027
Missing (%)45.9%
Memory size22.4 MiB
1.0
571095 
2.0
520526 
3.0
394511 
4.0
103319 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row4.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0571095
19.4%
2.0520526
 
17.7%
3.0394511
 
13.4%
4.0103319
 
3.5%
(Missing)1350027
45.9%

Length

2021-10-06T00:48:32.809958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:48:32.978997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0571095
35.9%
2.0520526
32.7%
3.0394511
24.8%
4.0103319
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1350027
Missing (%)45.9%
Memory size22.4 MiB
Maison
571095 
Appartement
520526 
Dépendance
394511 
Local industriel. commercial ou assimilé
103319 

Length

Max length40
Median length10
Mean length10.84036312
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppartement
2nd rowLocal industriel. commercial ou assimilé
3rd rowLocal industriel. commercial ou assimilé
4th rowMaison
5th rowMaison

Common Values

ValueCountFrequency (%)
Maison571095
19.4%
Appartement520526
 
17.7%
Dépendance394511
 
13.4%
Local industriel. commercial ou assimilé103319
 
3.5%
(Missing)1350027
45.9%

Length

2021-10-06T00:48:33.264576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T00:48:33.460623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
maison571095
28.5%
appartement520526
26.0%
dépendance394511
19.7%
assimilé103319
 
5.2%
ou103319
 
5.2%
commercial103319
 
5.2%
industriel103319
 
5.2%
local103319
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_reelle_bati
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4617
Distinct (%)0.4%
Missing1763264
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean116.3063006
Minimum1
Maximum271450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:33.715190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q150
median75
Q3103
95-th percentile187
Maximum271450
Range271449
Interquartile range (IQR)53

Descriptive statistics

Standard deviation723.634591
Coefficient of variation (CV)6.221800431
Kurtosis22869.31238
Mean116.3063006
Median Absolute Deviation (MAD)26
Skewness101.1116364
Sum136801099
Variance523647.0212
MonotonicityNot monotonic
2021-10-06T00:48:34.037998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8023116
 
0.8%
6022263
 
0.8%
7020895
 
0.7%
9020405
 
0.7%
10017568
 
0.6%
5017380
 
0.6%
6516321
 
0.6%
4015884
 
0.5%
7514777
 
0.5%
4513712
 
0.5%
Other values (4607)993893
33.8%
(Missing)1763264
60.0%
ValueCountFrequency (%)
1287
 
< 0.1%
2165
 
< 0.1%
3155
 
< 0.1%
4143
 
< 0.1%
5177
 
< 0.1%
6305
 
< 0.1%
7268
 
< 0.1%
8490
 
< 0.1%
9974
 
< 0.1%
103053
0.1%
ValueCountFrequency (%)
2714501
< 0.1%
1435681
< 0.1%
1271391
< 0.1%
1162011
< 0.1%
1146001
< 0.1%
1002001
< 0.1%
843201
< 0.1%
828961
< 0.1%
825982
< 0.1%
794621
< 0.1%

nombre_pieces_principales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct51
Distinct (%)< 0.1%
Missing1352568
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean2.357246473
Minimum0
Maximum96
Zeros504465
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:34.354583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum96
Range96
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.077591811
Coefficient of variation (CV)0.8813638436
Kurtosis10.67712223
Mean2.357246473
Median Absolute Deviation (MAD)2
Skewness0.8073559444
Sum3740738
Variance4.316387735
MonotonicityNot monotonic
2021-10-06T00:48:34.677868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0504465
 
17.2%
3266057
 
9.1%
4264996
 
9.0%
2191610
 
6.5%
5153481
 
5.2%
1113354
 
3.9%
658553
 
2.0%
721213
 
0.7%
87622
 
0.3%
92867
 
0.1%
Other values (41)2692
 
0.1%
(Missing)1352568
46.0%
ValueCountFrequency (%)
0504465
17.2%
1113354
 
3.9%
2191610
 
6.5%
3266057
9.1%
4264996
9.0%
5153481
 
5.2%
658553
 
2.0%
721213
 
0.7%
87622
 
0.3%
92867
 
0.1%
ValueCountFrequency (%)
962
< 0.1%
811
< 0.1%
721
< 0.1%
661
< 0.1%
611
< 0.1%
601
< 0.1%
531
< 0.1%
511
< 0.1%
472
< 0.1%
451
< 0.1%

code_nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing901816
Missing (%)30.7%
Memory size22.4 MiB
S
939239 
T
305255 
P
160055 
AB
128001 
J
102981 
Other values (22)
402131 

Length

Max length2
Median length1
Mean length1.209043993
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowL
3rd rowL
4th rowS
5th rowL

Common Values

ValueCountFrequency (%)
S939239
32.0%
T305255
 
10.4%
P160055
 
5.4%
AB128001
 
4.4%
J102981
 
3.5%
BT85411
 
2.9%
L85105
 
2.9%
AG73035
 
2.5%
VI36546
 
1.2%
BR30770
 
1.0%
Other values (17)91264
 
3.1%
(Missing)901816
30.7%

Length

2021-10-06T00:48:35.218573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s939239
46.1%
t305255
 
15.0%
p160055
 
7.9%
ab128001
 
6.3%
j102981
 
5.1%
bt85411
 
4.2%
l85105
 
4.2%
ag73035
 
3.6%
vi36546
 
1.8%
br30770
 
1.5%
Other values (17)91264
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing901816
Missing (%)30.7%
Memory size22.4 MiB
sols
939239 
terres
305255 
prés
160055 
terrains a bâtir
128001 
jardins
102981 
Other values (22)
402131 

Length

Max length19
Median length4
Mean length6.777070486
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlandes
2nd rowlandes
3rd rowlandes
4th rowsols
5th rowlandes

Common Values

ValueCountFrequency (%)
sols939239
32.0%
terres305255
 
10.4%
prés160055
 
5.4%
terrains a bâtir128001
 
4.4%
jardins102981
 
3.5%
taillis simples85411
 
2.9%
landes85105
 
2.9%
terrains d'agrément73035
 
2.5%
vignes36546
 
1.2%
futaies résineuses30770
 
1.0%
Other values (17)91264
 
3.1%
(Missing)901816
30.7%

Length

2021-10-06T00:48:35.487637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sols939239
37.2%
terres305359
 
12.1%
terrains201036
 
8.0%
prés162461
 
6.4%
a128001
 
5.1%
bâtir128001
 
5.1%
jardins102981
 
4.1%
taillis100034
 
4.0%
simples85411
 
3.4%
landes85387
 
3.4%
Other values (24)284942
 
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)0.1%
Missing2800349
Missing (%)95.3%
Memory size22.4 MiB
POTAG
28150 
PATUR
14516 
PARC
10980 
PIN
10183 
FRICH
9492 
Other values (119)
65808 

Length

Max length5
Median length5
Mean length4.459386612
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowJARD
2nd rowACACI
3rd rowACACI
4th rowPOTAG
5th rowFOSSE

Common Values

ValueCountFrequency (%)
POTAG28150
 
1.0%
PATUR14516
 
0.5%
PARC10980
 
0.4%
PIN10183
 
0.3%
FRICH9492
 
0.3%
VAOC7536
 
0.3%
IMM7155
 
0.2%
CHAT3953
 
0.1%
CHENE3282
 
0.1%
RUE3041
 
0.1%
Other values (114)40841
 
1.4%
(Missing)2800349
95.3%

Length

2021-10-06T00:48:35.758695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
potag28150
20.2%
patur14516
 
10.4%
parc10980
 
7.9%
pin10183
 
7.3%
frich9492
 
6.8%
vaoc7536
 
5.4%
imm7155
 
5.1%
chat3953
 
2.8%
chene3282
 
2.4%
rue3041
 
2.2%
Other values (114)40841
29.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct124
Distinct (%)0.1%
Missing2800349
Missing (%)95.3%
Memory size22.4 MiB
Jardin potager
28150 
Pâture plantée
14516 
Parc
10980 
Pins
10183 
Friche
9492 
Other values (119)
65808 

Length

Max length38
Median length14
Mean length13.32630149
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowJardin d'agrément
2nd rowAcacias
3rd rowAcacias
4th rowJardin potager
5th rowFosse

Common Values

ValueCountFrequency (%)
Jardin potager28150
 
1.0%
Pâture plantée14516
 
0.5%
Parc10980
 
0.4%
Pins10183
 
0.3%
Friche9492
 
0.3%
Vins d'appellation d'origine contrôlée7536
 
0.3%
Dépendances d'ensemble immobilier7155
 
0.2%
Châtaigneraie3953
 
0.1%
Chênes3282
 
0.1%
Rue3041
 
0.1%
Other values (114)40841
 
1.4%
(Missing)2800349
95.3%

Length

2021-10-06T00:48:36.049184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardin29525
 
11.3%
potager28150
 
10.8%
pâture14516
 
5.6%
plantée14516
 
5.6%
parc10984
 
4.2%
pins10183
 
3.9%
friche9492
 
3.6%
vins7704
 
3.0%
ou7657
 
2.9%
contrôlée7536
 
2.9%
Other values (159)120562
46.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_terrain
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct44269
Distinct (%)2.2%
Missing901874
Missing (%)30.7%
Infinite0
Infinite (%)0.0%
Mean3075.789123
Minimum1
Maximum4301668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2021-10-06T00:48:36.355254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1235
median630
Q31980
95-th percentile12895
Maximum4301668
Range4301667
Interquartile range (IQR)1745

Descriptive statistics

Standard deviation15396.96176
Coefficient of variation (CV)5.005857406
Kurtosis28269.44841
Mean3075.789123
Median Absolute Deviation (MAD)509
Skewness121.0253572
Sum6267240220
Variance237066431.4
MonotonicityNot monotonic
2021-10-06T00:48:36.637318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50038122
 
1.3%
100018325
 
0.6%
8005961
 
0.2%
6005590
 
0.2%
125010
 
0.2%
4004770
 
0.2%
134674
 
0.2%
1004605
 
0.2%
7004602
 
0.2%
3004490
 
0.2%
Other values (44259)1941455
66.0%
(Missing)901874
30.7%
ValueCountFrequency (%)
14187
0.1%
23408
0.1%
33305
0.1%
43435
0.1%
53491
0.1%
63401
0.1%
73240
0.1%
83355
0.1%
93187
0.1%
104321
0.1%
ValueCountFrequency (%)
43016686
< 0.1%
41889391
 
< 0.1%
39230361
 
< 0.1%
37964901
 
< 0.1%
30928941
 
< 0.1%
26330471
 
< 0.1%
24553151
 
< 0.1%
24453451
 
< 0.1%
23431553
< 0.1%
23027351
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1594814
Distinct (%)56.4%
Missing112870
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.284574988
Minimum-63.149476
Maximum55.828143
Zeros0
Zeros (%)0.0%
Negative626728
Negative (%)21.3%
Memory size22.4 MiB
2021-10-06T00:48:36.951389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-63.149476
5-th percentile-2.1288005
Q10.33686775
median2.359149
Q34.560187
95-th percentile6.59459925
Maximum55.828143
Range118.977619
Interquartile range (IQR)4.22331925

Descriptive statistics

Standard deviation6.230969229
Coefficient of variation (CV)2.727408494
Kurtosis69.51076523
Mean2.284574988
Median Absolute Deviation (MAD)2.094442
Skewness-1.860785691
Sum6457597.938
Variance38.82497753
MonotonicityNot monotonic
2021-10-06T00:48:37.232450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.353493930
 
< 0.1%
2.487316757
 
< 0.1%
2.431922728
 
< 0.1%
2.392866656
 
< 0.1%
2.331678650
 
< 0.1%
4.33429637
 
< 0.1%
4.827475608
 
< 0.1%
2.372733552
 
< 0.1%
-0.519837510
 
< 0.1%
2.12356492
 
< 0.1%
Other values (1594804)2820088
95.9%
(Missing)112870
 
3.8%
ValueCountFrequency (%)
-63.1494763
 
< 0.1%
-63.1489018
< 0.1%
-63.1485044
< 0.1%
-63.1484814
< 0.1%
-63.1405414
< 0.1%
-63.139536
< 0.1%
-63.1349391
 
< 0.1%
-63.1346521
 
< 0.1%
-63.1336472
 
< 0.1%
-63.1333371
 
< 0.1%
ValueCountFrequency (%)
55.8281431
< 0.1%
55.8278161
< 0.1%
55.8268641
< 0.1%
55.8266791
< 0.1%
55.8262481
< 0.1%
55.8241851
< 0.1%
55.8237091
< 0.1%
55.8236071
< 0.1%
55.8234031
< 0.1%
55.8229131
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1549543
Distinct (%)54.8%
Missing112870
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean46.16768913
Minimum-21.386074
Maximum51.082118
Zeros0
Zeros (%)0.0%
Negative13200
Negative (%)0.4%
Memory size22.4 MiB
2021-10-06T00:48:37.607535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-21.386074
5-th percentile43.22609175
Q144.7402405
median46.7351275
Q348.700214
95-th percentile49.8918253
Maximum51.082118
Range72.468192
Interquartile range (IQR)3.9599735

Descriptive statistics

Standard deviation5.605373036
Coefficient of variation (CV)0.1214133335
Kurtosis100.0702272
Mean46.16768913
Median Absolute Deviation (MAD)1.9721055
Skewness-9.041697244
Sum130497959.4
Variance31.42020688
MonotonicityNot monotonic
2021-10-06T00:48:37.893599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.388626930
 
< 0.1%
48.898406757
 
< 0.1%
48.986808731
 
< 0.1%
48.811396665
 
< 0.1%
48.786767651
 
< 0.1%
43.823295637
 
< 0.1%
43.953439608
 
< 0.1%
48.974006550
 
< 0.1%
47.487243510
 
< 0.1%
48.864581493
 
< 0.1%
Other values (1549533)2820076
95.9%
(Missing)112870
 
3.8%
ValueCountFrequency (%)
-21.3860741
 
< 0.1%
-21.3856272
< 0.1%
-21.3848154
< 0.1%
-21.3847231
 
< 0.1%
-21.3846612
< 0.1%
-21.3845821
 
< 0.1%
-21.3844891
 
< 0.1%
-21.3844471
 
< 0.1%
-21.3834681
 
< 0.1%
-21.3832551
 
< 0.1%
ValueCountFrequency (%)
51.0821183
 
< 0.1%
51.0819476
< 0.1%
51.0818056
< 0.1%
51.0817655
< 0.1%
51.081712
 
< 0.1%
51.08163110
< 0.1%
51.0815766
< 0.1%
51.0811021
 
< 0.1%
51.0808751
 
< 0.1%
51.0808091
 
< 0.1%

Interactions

2021-10-06T00:46:19.258880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:36.774081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:58.295322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:22.306651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:40.799428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:02.400728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:08.866021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:17.171138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:23.714599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:30.119607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:36.315247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:44.266760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:04.883619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:20.247611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:37.575650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:55.910857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:21.702720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:38.958410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:00.911663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:23.966491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:43.246736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:02.825286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:09.409143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:17.619238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:24.120711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:30.509694image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:36.708857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:46.507430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:06.107932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:21.818499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:39.533898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:58.371312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:23.295587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:40.415122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:02.737718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:25.722887image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:44.880615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:03.224452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:10.019086image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:18.058339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:24.555522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:30.888781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:37.153467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:48.050865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:07.311222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:23.337811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:40.590289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:59.982716image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:25.844689image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:42.579169image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:05.481279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:27.263131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:47.282644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:03.653613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:10.525639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:18.475449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:24.978633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:31.261865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:37.523058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:50.238671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:08.543620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:24.894179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:42.383621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:02.377455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:26.418820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:43.091303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:05.993441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:27.672415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:47.768751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:04.044352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:10.891265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:18.840531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:25.354733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:31.641951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:37.911207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:50.661071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:09.174762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:25.316153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:42.810718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:02.831559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:26.933955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:43.590450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:06.567568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:28.254061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:48.276372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:04.396628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:11.398890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:19.214616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:25.696344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:31.974536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:38.228278image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:51.241850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:09.727909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:25.850602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:43.210808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:03.353676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:27.366222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:44.007546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:06.984693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:28.676304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:48.676464image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:04.808496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:11.752994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:19.602265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:26.076941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:32.343635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:38.569354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:51.657454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:10.158436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:26.284597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:45:43.628436image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:03.771773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:27.758311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:42:44.434641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:07.388453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:29.048407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:43:49.059064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:44:05.234104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:44:42.112232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T00:45:53.434389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T00:46:16.869165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-10-06T00:48:38.229187image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-06T00:48:38.983355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-06T00:48:39.691734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-06T00:48:40.394853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-06T00:48:40.809759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-06T00:46:47.496752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-06T00:47:04.944934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-06T00:47:53.116366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-06T00:48:05.529670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
02016-12016-01-081Vente40000.077.0NaNRUE TONY REVILLON05601750.01370Saint-Laurent-sur-Saône1NaNNaN013700000A0253NaNNaN441.55NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement50.02.0NaNNaNNaNNaNNaN4.84060646.304486
12016-22016-01-111Vente1677.0NaNNaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1043NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN1486.05.32246446.041159
22016-22016-01-111Vente1677.0NaNNaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1157NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN3904.05.31703846.033713
32016-22016-01-111Vente1677.0NaNNaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1159NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN1779.05.32417746.041343
42016-22016-01-111Vente1677.05246.0NaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1160NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN04.0Local industriel. commercial ou assimiléNaN0.0SsolsNaNNaN838.05.32366646.040803
52016-22016-01-111Vente1677.0NaNNaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1162NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN1842.05.32196646.039391
62016-22016-01-111Vente1677.0NaNNaNLES BROTTEAUXB0111160.01430Varambon1NaNNaN014300000C1163NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNLlandesNaNNaN419.05.32281946.041144
72016-32016-01-081Vente7915000.06.0NaNAV AMEDEE MERCIER02251000.01053Bourg-en-Bresse1NaNNaN01053000BD0293NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNSsolsNaNNaN228.05.23483246.204876
82016-32016-01-081Vente7915000.032.0NaNBD ST NICOLAS34301000.01053Bourg-en-Bresse1NaNNaN01053000BD0294NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN04.0Local industriel. commercial ou assimilé3836.00.0SsolsNaNNaN1151.05.23483446.204895
92016-32016-01-081Vente7915000.06.0NaNAV AMEDEE MERCIER02251000.01053Bourg-en-Bresse1NaNNaN01053000BD0300NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNSsolsNaNNaN2.05.23541846.205421

Last rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
29394682016-12742382016-12-231Vente12200000.0104.0NaNRUE DE RICHELIEU821275002.075102Paris 2e Arrondissement75NaNNaN75102000AG0044NaNNaN50NaNNaNNaNNaNNaNNaNNaNNaNNaN14.0Local industriel. commercial ou assimilé12.00.0NaNNaNNaNNaNNaN2.34029948.871080
29394692016-12742382016-12-231Vente12200000.0104.0NaNRUE DE RICHELIEU821275002.075102Paris 2e Arrondissement75NaNNaN75102000AG0044NaNNaN41NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN2.34029948.871080
29394702016-12742382016-12-231Vente12200000.0104.0NaNRUE DE RICHELIEU821275002.075102Paris 2e Arrondissement75NaNNaN75102000AG0044NaNNaN43NaN44NaN45NaNNaNNaNNaNNaN34.0Local industriel. commercial ou assimilé36.00.0NaNNaNNaNNaNNaN2.34029948.871080
29394712016-12742382016-12-231Vente12200000.0104.0NaNRUE DE RICHELIEU821275002.075102Paris 2e Arrondissement75NaNNaN75102000AG0044NaNNaN51NaNNaNNaNNaNNaNNaNNaNNaNNaN14.0Local industriel. commercial ou assimilé12.00.0NaNNaNNaNNaNNaN2.34029948.871080
29394722016-12742392016-12-191Vente130000.05.0NaNIMP SAINT DENIS852475002.075102Paris 2e Arrondissement75NaNNaN75102000AM0174NaNNaN1018.522NaNNaNNaNNaNNaNNaNNaN24.0Local industriel. commercial ou assimilé24.00.0NaNNaNNaNNaNNaN2.34992048.865545
29394732016-12742402016-10-011Adjudication2715000.03.0NaNRUE DE LA BOURSE121775002.075102Paris 2e Arrondissement75NaNNaN75102000AF0016NaNNaN11NaN5NaNNaNNaNNaNNaNNaNNaN24.0Local industriel. commercial ou assimilé176.00.0NaNNaNNaNNaNNaN2.33985448.869299
29394742016-12742402016-10-011Adjudication2715000.03.0NaNRUE DE LA BOURSE121775002.075102Paris 2e Arrondissement75NaNNaN75102000AF0016NaNNaN12NaN6NaNNaNNaNNaNNaNNaNNaN24.0Local industriel. commercial ou assimilé176.00.0NaNNaNNaNNaNNaN2.33985448.869299
29394752016-12742412016-12-231Vente30000.046.0NaNRUE DES ARCHIVES042075004.075104Paris 4e Arrondissement75NaNNaN75104000AH0001NaNNaN1014.00NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement10.01.0NaNNaNNaNNaNNaN2.35643348.859425
29394762016-12742422016-12-151Adjudication9100.0152.0NaNRUE SAINT DENIS852575002.075102Paris 2e Arrondissement75NaNNaN75102000AM0091NaNNaN36NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement4.01.0NaNNaNNaNNaNNaN2.35080048.865636
29394772016-12742432016-07-281Vente4000.017.0NaNRUE BLONDEL102175002.075102Paris 2e Arrondissement75NaNNaN75102000AP0053NaNNaN22NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement15.02.0NaNNaNNaNNaNNaN2.35308248.868707